Power battery thermal management cooling liquid loop degassing control method and system, terminal and medium

Abstract

The invention belongs to the technical field of power battery thermal management, and particularly discloses a degassing control method and system for a power battery thermal management cooling liquid loop, a terminal and a medium. Comprising the following steps: acquiring historical operation data, acquiring loop state data and power battery operation condition data in real time, preprocessing and fusing the data, generating a gas state quantity, and generating a loop gas resistance risk quantity based on the gas state quantity; performing trend prediction on the gas state quantity based on the historical operation data, the current loop state data and the working condition data, and determining a pre-start degassing criterion; and according to the gas state quantity, the risk quantity, the gas trend prediction result and a combined judgment result of a preset threshold rule, the degassing control level is determined, vacuum grading degassing control is executed when a normal degassing condition is met, and controllable pulse pressure relief and directional exhaust control is executed when an abnormal safety disposal condition is met. And quantitative characterization, trend prediction and hierarchical control of the gas state of the power battery thermal management cooling liquid loop are realized.

Description

Technical Field

[0001] This invention belongs to the field of power battery thermal management technology, specifically relating to a method, system, terminal, and medium for degassing control of the coolant circuit in power battery thermal management. Background Technology

[0002] As pure electric vehicles develop towards higher energy density, higher power output, and longer lifespan, the power battery system continuously operates under complex and variable conditions. To ensure temperature consistency and operational safety of the power battery under different conditions, vehicles typically employ a battery thermal management system. This system regulates heat exchange in the power battery through the coolant circuit to suppress localized overheating, delay performance degradation, and improve overall system reliability. During long-term operation, the coolant circuit is inevitably affected by factors such as temperature changes, pressure fluctuations, and flow disturbances. Changes in the internal gas state of the coolant circuit are gradually becoming a key factor affecting thermal management performance and system stability.

[0003] In existing technologies, the solutions to the problem of gas in the coolant circuit are mostly focused on structural design or passive venting methods, such as setting up venting valves, replenishing fluid devices, or performing regular maintenance to remove gas from the circuit.

[0004] These types of solutions typically rely on trigger control based on a single parameter or static threshold, making it difficult to reflect the cumulative effects and evolution trends of gas in the circuit as operating conditions change. Furthermore, they are prone to delayed venting timing or false triggering under complex operating conditions. On the other hand, in abnormal operating conditions or safety incidents in power battery systems, sudden pressure changes, abnormal gas generation, and flow imbalances in the coolant circuit often exhibit multi-parameter coupling characteristics. Existing technologies mostly employ uniform emergency venting or depressurization strategies to handle abnormal operating conditions, making it difficult to ensure both safety and stable system operation and control accuracy. Summary of the Invention

[0005] This invention addresses the problems in the prior art by providing a method, system, terminal, and medium for degassing control of the coolant circuit in power battery thermal management. It solves the problems of existing technologies that rely on triggering control based on a single parameter or static threshold, which fails to reflect the cumulative effects and evolution trends of gas in the circuit as operating conditions change. Furthermore, it addresses the issue of delayed venting timing or false triggering under complex conditions. Simultaneously, it resolves the problem that in abnormal operating conditions or safety events in power battery systems, sudden pressure changes, abnormal gas generation, and flow imbalances in the coolant circuit often exhibit multi-parameter coupling characteristics. Existing technologies often employ uniform emergency venting or pressure relief strategies for handling abnormal conditions, making it difficult to simultaneously ensure system stability and control accuracy while guaranteeing safety.

[0006] The technical solution adopted in this invention is as follows: In a first aspect, this application provides a method for controlling the degassing of the coolant circuit in a power battery thermal management system, the method comprising the following steps: Acquire historical operating data, and acquire in real time circuit status data to characterize the gas generation and exhaust status of the power battery thermal management coolant circuit, as well as operating condition data to characterize the power battery operating conditions. The loop state data and operating condition data are preprocessed and fused to generate a gas state quantity to characterize the degree of gas accumulation in the coolant loop. Based on the gas state quantity, a risk quantity to characterize the risk of gas resistance in the loop is generated. Based on historical operating data, current loop status data, and operating condition data, the trend of gas state quantities is predicted to obtain gas trend prediction results, and the pre-start degassing criteria are determined accordingly. The gas state quantity, risk quantity, gas trend prediction results are combined with preset threshold rules to determine the degassing control level. When the degassing control level meets the normal degassing conditions, the degassing control parameters are determined based on the deviation between the gas state quantity and the target state quantity, and vacuum staged degassing control is executed. When the degassing control level meets the abnormal safety handling conditions, the handling control parameters are determined based on the circuit pressure and pressure change characteristics, and controllable pulse depressurization and directional exhaust control are executed.

[0007] Furthermore, the steps for acquiring and preprocessing loop status data and operating condition data include: Within the same control cycle, multi-source raw data reflecting the gas state, pressure state, and flow state in the coolant circuit are acquired simultaneously. The original data from multiple sources are processed sequentially with timestamp alignment, filtering, and outlier identification. The identified outliers are then removed or replaced. The processed multi-source data is combined to generate a data set that characterizes the overall operating status of the coolant circuit under the current control cycle, and the data set is stored as a loop status snapshot.

[0008] Furthermore, the steps for generating gaseous state quantities include: Normalize the data reflecting changes in gas content in the coolant in the loop state snapshot and calculate the gas content change data between adjacent control cycles; Perform trend extraction processing on the data reflecting the bubble distribution and change characteristics in the loop state snapshot, and calculate the bubble distribution change data between adjacent control cycles; Based on the fusion weight, the gas content change data and bubble distribution change data are linearly combined to obtain the intermediate gas fusion amount; The pressure change characteristics of the coolant circuit between adjacent control cycles are introduced as a modulation factor to correct the intermediate gas fusion amount and generate gas state variables. Among them, gas state quantities Determined according to the following relationship:

[0009] in, Data on changes in gas content, Data on bubble distribution changes, To represent the change in loop pressure, , and To integrate weights, This is the preset modulation coefficient.

[0010] The gas state variables are updated in each control cycle.

[0011] Furthermore, the risk quantity is generated based on the gas state quantity, and the trend of the gas state quantity is predicted, including the following steps: A gas state change model is constructed based on the time sequence of gas state variables in historical operating data. Within each control cycle, the gas state quantity acquired in the current control cycle is used as the initial state of the model, and the current operating condition data is used as the model input to update the gas state change model. Based on the updated gas state change model, the gas state variables are predicted for at least one subsequent control period. Based on the gas state variables in the current control cycle, the predicted gas state variables, and the pressure and flow change characteristics of the coolant circuit, calculate the risk quantity used to characterize the operating risk level of the coolant circuit. The gas state change model adopts a discrete state recursive form, which satisfies the following relationship:

[0012] in, The data represents the current operating conditions, while A and B are state transition coefficients determined based on historical operating data. Risk level Determined according to the following relationship:

[0013] in, , , These are preset weighting coefficients.

[0014] Furthermore, the determination of the pre-start degassing criteria includes: Based on the gas state change model, the predicted gas state quantities are obtained for multiple predictive control cycles within a preset prediction time window. Compare the predicted gas state quantity corresponding to each predictive control cycle with the preset gas prediction threshold. The number of control cycles in which the predicted gas state quantity exceeds the gas prediction threshold within the prediction time window is counted. when At that time, the pre-start degassing criterion is determined to be valid; in, This is an indicator function that takes the value 1 when the condition is true and 0 otherwise. For the predicted gas state quantity in the k-th predictive control cycle; N is the gas prediction threshold; M is the prediction time window length; and M is the preset number of consecutive judgments.

[0015] Furthermore, the degassing control level is determined, including: Generate corresponding gas risk indicators based on gas state variables. Based on the characteristics of loop pressure change and flow, corresponding loop operation risk indicators are generated. Generate corresponding forecast risk indicators based on gas trend forecast results. The gas risk index, the loop operation risk index and the predicted risk index are weighted and summed to obtain a comprehensive risk score; The comprehensive risk score is compared with multiple preset risk ranges to determine the degassing control level. The comprehensive risk score is determined according to the following relationship:

[0016] in, To predict the statistical values ​​of gas state quantities, , , These are preset weighting coefficients.

[0017] Furthermore, the process of performing vacuum staged degassing control or controllable pulse depressurization and directional exhaust control also includes: After the control execution is completed, obtain the loop operation status data corresponding to the current control cycle; The control deviation is obtained by comparing the loop operating status data with the loop status data before control execution. The degassing control parameters for the next control cycle are updated based on the control deviation. The degassing control parameters are updated according to the following adaptive update relationship:

[0018] in, These are the degassing control parameters for the current control cycle. For the target gas state variables, This is the preset update coefficient.

[0019] Secondly, this application provides a degassing control system for the thermal management coolant circuit of a power battery, used to implement the degassing control method for the thermal management coolant circuit of a power battery as described in the first aspect. The system includes: The intelligent sensing layer is configured to collect multi-source sensing data to characterize the gas state, pressure state and flow state of the coolant circuit within the same control cycle, and perform time alignment, filtering and outlier removal on the multi-source sensing data to generate circuit state data. The edge intelligent decision layer communicates with the intelligent perception layer and is configured to receive loop status data and power battery operating condition data, fuse them to generate gas state variables, build and update the gas state change model based on historical operating data, and obtain the predicted gas state variables. Risk quantities are generated based on gas state quantities, predicted gas state quantities, and loop pressure and flow change characteristics. Degassing control levels are determined and degassing control commands are generated according to preset threshold rules. The high-efficiency execution layer communicates with the edge intelligent decision-making layer and is configured to receive degassing control commands. When the degassing control level corresponds to normal degassing conditions, it performs vacuum staged degassing control and performs controllable pulse depressurization and directional exhaust control when the degassing control level corresponds to abnormal safety handling conditions. The safety protection and redundancy design layer is configured to monitor key operating parameters of the coolant circuit during system operation and trigger control strategy verification or degradation processing when an abnormal operating state is detected. The cloud platform and data closed-loop layer are configured to store and analyze system operation data and degassing event data, update the parameters and threshold rules of the gas state change model based on the analysis results, and send the updated results to the edge intelligent decision layer.

[0020] Thirdly, this application provides a terminal, including: The memory is used to store the degassing control program for the thermal management coolant circuit of the power battery; A processor is configured to execute the steps of the power battery thermal management coolant circuit degassing control method as described in the first aspect when performing the power battery thermal management coolant circuit degassing control device.

[0021] Fourthly, this application provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the power battery thermal management coolant circuit degassing control method as described in the first aspect.

[0022] As can be seen from the above technical solutions, the advantages of the present invention are: By collecting and fusing multi-source operating data of the power battery thermal management coolant circuit, a gas state quantity is constructed to characterize the degree of gas accumulation in the coolant circuit. Based on this, a gas state change model is introduced to model and predict the gas state evolution process, so that the gas state in the coolant circuit is transformed from a traditional qualitative judgment to a quantifiable and updatable state representation, thereby providing a clear data basis and judgment basis for degassing control.

[0023] By jointly determining the gas state quantity, the risk quantity generated based on pressure and flow changes, and the gas trend prediction results, and accordingly determining the degassing control level, hierarchical control of the degassing behavior of the coolant circuit is realized. This enables the degassing control process to be dynamically adjusted according to the circuit operating status, avoiding the control lag or false triggering problems caused by relying on a single parameter or fixed threshold, and improving the rationality and stability of the degassing control decision.

[0024] Under normal operating conditions, by determining the degassing control parameters based on the deviation between the gas state quantity and the target state quantity and executing vacuum staged degassing control, the degassing process can be adjusted according to the degree of gas accumulation, which helps to maintain the stable operating state of the coolant circuit and reduce the impact of gas on cooling performance and flow continuity.

[0025] In abnormal operating conditions or safety handling scenarios, abnormal handling control parameters are generated based on the circuit pressure and its change characteristics, and controllable pulse pressure relief and directional exhaust control are executed to make the abnormal gas release process controllable and targeted, which helps to reduce the disturbance to the overall operating state of the coolant circuit while meeting safety requirements. Attached Figure Description

[0026] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a flowchart of the degassing control method for the power battery thermal management coolant circuit of the present invention; Figure 2 This is a flowchart of the intelligent perception layer runtime of the present invention; Figure 3 This is a flowchart of the runtime process of the edge intelligent decision-making layer of the present invention; Figure 4 This is a flowchart of the high-efficiency execution layer runtime of the present invention; Figure 5This is a flowchart illustrating the security protection and redundancy design of the present invention during runtime. Figure 6 This is a flowchart of the cloud platform and data closed-loop layer runtime of the present invention; Figure 7 This is a structural diagram of the degassing control system for the power battery thermal management coolant circuit of the present invention; Figure 8 This is a flowchart illustrating the operation of the degassing control system for the power battery thermal management coolant circuit of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Please see Figures 1-6 As shown, this application provides a method for degassing control of the coolant circuit in the thermal management of a power battery, including the following steps: Step S1: Obtain historical operating data, and obtain in real time the circuit status data used to characterize the gas generation and exhaust status of the power battery thermal management coolant circuit, as well as the operating condition data used to characterize the operating conditions of the power battery. In this embodiment, historical operating data is used to reflect the long-term operating characteristics of the power battery thermal management coolant circuit under different operating stages. This historical operating data can be derived from coolant circuit status information and power battery operating condition information recorded during previous vehicle operation. Real-time acquired circuit status data is used to characterize the immediate operating status of the coolant circuit within the current control cycle, specifically including operating data reflecting the gas state, pressure state, and flow state in the coolant circuit. The operating condition data is used to characterize the current operating conditions of the power battery, reflecting the changing demands of the power battery on the thermal management system under different loads and operating modes. In a specific embodiment, the system synchronously collects the aforementioned circuit status data and operating condition data at a preset control cycle during vehicle operation, and associates and stores them with historical operating data to form a data foundation for subsequent processing.

[0030] Step S2: Preprocess and fuse the loop state data and operating condition data to generate a gas state quantity that characterizes the degree of gas accumulation in the coolant loop, and generate a risk quantity that characterizes the risk of gas resistance in the loop based on the gas state quantity. In this embodiment, the preprocessing of loop state data and operating condition data includes time alignment, outlier identification, and filtering of data from different sources to reduce the impact of instantaneous fluctuations or noise on subsequent analysis. The preprocessed loop state data and operating condition data are then fused and calculated to form a gas state quantity that comprehensively reflects the gas accumulation level within the coolant loop. This gas state quantity is used to quantitatively describe the gas state in the coolant loop, rather than relying solely on a single parameter for judgment. Furthermore, based on the gas state quantity and the characteristics of the gas's influence on flow during coolant loop operation, a risk quantity is generated to characterize the risk of gas resistance in the loop. In one embodiment, as the gas state quantity continuously increases with the control cycle, the corresponding risk quantity increases, reflecting the change in the degree to which the gas affects the operation of the coolant loop.

[0031] Step S3: Based on historical operating data, current loop status data, and operating condition data, perform trend prediction on gas state quantities to obtain gas trend prediction results, and determine the pre-start degassing criteria accordingly. In this embodiment, a predictive model describing the evolution of gas states is constructed by analyzing the characteristics of gas state variables over time in historical operating data. Within each control cycle, the current loop state data and operating condition data are input into this predictive model to predict the trend of gas state variables in subsequent control cycles, thus obtaining a gas trend prediction result. This gas trend prediction result reflects the evolution direction and rate of change of gas state variables in the short term. In a specific embodiment, when the prediction result shows that the gas state variables continue to rise and approach a preset threshold over several future control cycles, the pre-start degassing criterion can be determined to be met, thereby enabling control preparation before the gas problem becomes explicit.

[0032] Step S4: Combine the gas state quantity, risk quantity, and gas trend prediction results with the preset threshold rules to determine the degassing control level. In this embodiment, the degassing control level is used to characterize the type of degassing control strategy required for the current coolant circuit. Specifically, the gas state quantity, corresponding risk quantity, and gas trend prediction results within the current control cycle are jointly judged with pre-set threshold rules to comprehensively reflect the operational risk level of the coolant circuit. In one embodiment, the degassing control level can be divided into a normal degassing level or an abnormal safety handling level based on the joint judgment result, thereby avoiding the misjudgment problem caused by making degassing control decisions based on only a single parameter.

[0033] Step S5: When the degassing control level meets the normal degassing conditions, the degassing control parameters are determined based on the deviation between the gas state quantity and the target state quantity, and vacuum staged degassing control is executed. In this embodiment, when the joint determination result indicates that the coolant circuit is at the normal degassing control level, the degassing control parameters are determined based on the deviation between the current gas state quantity and the preset target state quantity. These degassing control parameters are used to adjust the execution mode of the vacuum staged degassing process, ensuring that the degassing process matches the degree of gas accumulation. In one embodiment, when the gas state quantity is slightly higher than the target state quantity, the system performs a lower-intensity vacuum degassing operation; when the gas state quantity is significantly higher than the target state quantity, the degassing intensity is increased accordingly, thereby achieving stable regulation of the gas state in the coolant circuit.

[0034] When the degassing control level meets the abnormal safety handling conditions, the handling control parameters are determined based on the circuit pressure and pressure change characteristics, and controllable pulse depressurization and directional exhaust control are executed.

[0035] In this embodiment, when the joint judgment result indicates that the coolant circuit is at an abnormal safety handling level, it indicates that there may be a high operational risk within the coolant circuit. At this time, corresponding handling control parameters are determined based on the circuit pressure and its change characteristics to quickly intervene in the abnormal gas state. In one embodiment, by executing a controllable pulse pressure relief method, the abnormal pressure in the coolant circuit is released in stages, and the abnormal gas is guided to a preset emission path through directional exhaust control, thereby completing the degassing handling under abnormal operating conditions while ensuring system safety.

[0036] In some embodiments, the step of acquiring loop status data and operating condition data and performing preprocessing includes: Within the same control cycle, multi-source raw data reflecting the gas state, pressure state, and flow state in the coolant circuit are acquired simultaneously. The original data from multiple sources are processed sequentially with timestamp alignment, filtering, and outlier identification. The identified outliers are then removed or replaced. The processed multi-source data is combined to generate a data set that characterizes the overall operating status of the coolant circuit under the current control cycle, and the data set is stored as a loop status snapshot.

[0037] In this embodiment, the synchronous acquisition of multi-source raw data ensures the comparability of data from different sources within the same control cycle, thereby avoiding judgment bias caused by inconsistent sampling times. Timestamp alignment maps various types of raw data to a unified time base, filtering suppresses transient fluctuations during operation, and outlier identification identifies abnormal data caused by sensor jitter or communication interference. In one specific embodiment, when a certain type of raw data deviates significantly from its historical distribution within a single control cycle, this data is marked as abnormal and replaced with reasonable data from adjacent cycles, thus ensuring the stability and continuity of the loop state snapshot.

[0038] In some embodiments, the step of generating gas state quantities includes: Normalize the data reflecting changes in gas content in the coolant in the loop state snapshot and calculate the gas content change data between adjacent control cycles; Perform trend extraction processing on the data reflecting the bubble distribution and change characteristics in the loop state snapshot, and calculate the bubble distribution change data between adjacent control cycles; Based on the fusion weight, the gas content change data and bubble distribution change data are linearly combined to obtain the intermediate gas fusion amount; The pressure change characteristics of the coolant circuit between adjacent control cycles are introduced as a modulation factor to correct the intermediate gas fusion amount and generate gas state variables. Among them, gas state quantities Determined according to the following relationship:

[0039] in, Data on changes in gas content, Data on bubble distribution changes, To represent the change in loop pressure, , and To integrate weights, This is the preset modulation coefficient.

[0040] The gas state variables are updated in each control cycle.

[0041] In this embodiment, by processing changes in gas content and bubble distribution separately, data from different sources and with different physical meanings can be fused within the same dimensional system. Normalization is used to eliminate the influence of different data scales on the fusion result, and trend extraction is used to reflect the evolution characteristics of bubble distribution over time. In a specific embodiment, when the gas content changes significantly while the bubble distribution changes relatively gradually, the fusion result mainly reflects the trend of gas precipitation; when the bubble distribution changes significantly, the fusion result can reflect the impact of bubble aggregation on the loop operating state. By introducing pressure change characteristics to correct intermediate fusion values, the gas state variables can comprehensively reflect the correlation between gas changes and the loop mechanical state.

[0042] In some embodiments, generating risk quantities based on gas state quantities and predicting trends in these gas state quantities includes the following steps: A gas state change model is constructed based on the time sequence of gas state variables in historical operating data. Within each control cycle, the gas state quantity acquired in the current control cycle is used as the initial state of the model, and the current operating condition data is used as the model input to update the gas state change model. Based on the updated gas state change model, the gas state variables are predicted for at least one subsequent control period. Based on the gas state variables in the current control cycle, the predicted gas state variables, and the pressure and flow change characteristics of the coolant circuit, calculate the risk quantity used to characterize the operating risk level of the coolant circuit. The gas state change model adopts a discrete state recursive form, which satisfies the following relationship:

[0043] in, The data represents the current operating conditions, while A and B are state transition coefficients determined based on historical operating data. Risk level Determined according to the following relationship:

[0044] in, , , These are preset weighting coefficients.

[0045] In this embodiment, the gas state change model is used to describe the evolution of gas state variables over time, enabling the system to predict future trends based on the current state. In one specific embodiment, historical operating data is used to characterize the changes in gas state variables under different operating conditions, while current operating condition data is used to reflect the impact of current operating conditions on the gas evolution process. By combining the predicted gas state variables with the current gas state variables and analyzing loop pressure and flow change characteristics, the risk quantity can simultaneously reflect the combined impact of gas accumulation level, gas change rate, and loop operating state.

[0046] In some embodiments, the determination of the pre-start degassing criterion includes: Based on the gas state change model, the predicted gas state quantities are obtained for multiple predictive control cycles within a preset prediction time window. Compare the predicted gas state quantity corresponding to each predictive control cycle with the preset gas prediction threshold. The number of control cycles in which the predicted gas state quantity exceeds the gas prediction threshold within the prediction time window is counted. when At that time, the pre-start degassing criterion is determined to be valid; in, This is an indicator function that takes the value 1 when the condition is true and 0 otherwise. For the predicted gas state quantity in the k-th predictive control cycle; N is the gas prediction threshold; M is the prediction time window length; and M is the preset number of consecutive judgments.

[0047] In this embodiment, by setting a prediction time window, the determination of the pre-start degassing criterion does not depend on a single prediction result, but is based on the comprehensive performance of multiple prediction cycles. In one specific embodiment, when the prediction result only occasionally exceeds the threshold within the time window, the pre-start degassing is not triggered; only when the prediction result continuously exceeds the threshold in multiple consecutive or cumulative cycles is the pre-start degassing criterion determined to be valid, thereby avoiding false triggering caused by short-term fluctuations.

[0048] In some embodiments, determining the degassing control level includes: Generate corresponding gas risk indicators based on gas state variables. Based on the characteristics of loop pressure change and flow, corresponding loop operation risk indicators are generated. Generate corresponding forecast risk indicators based on gas trend forecast results. The gas risk index, the loop operation risk index and the predicted risk index are weighted and summed to obtain a comprehensive risk score; The comprehensive risk score is compared with multiple preset risk ranges to determine the degassing control level. The comprehensive risk score is determined according to the following relationship:

[0049] in, To predict the statistical values ​​of gas state quantities, , , These are preset weighting coefficients.

[0050] In this embodiment, by generating gas risk indicators, loop operation risk indicators, and predicted risk indicators respectively, risk information from different sources can be uniformly incorporated into control decisions. In one specific embodiment, when the comprehensive risk score is in a low range, the system maintains normal operation; when the comprehensive risk score enters the middle range, the system enters the normal gas removal control level; when the comprehensive risk score enters the high range, the system enters the abnormal safety handling control level, thereby realizing a graded and differentiated gas removal control strategy.

[0051] In some embodiments, the process of performing vacuum staged degassing control or controllable pulse depressurization and directional exhaust control further includes: After the control execution is completed, obtain the loop operation status data corresponding to the current control cycle; The control deviation is obtained by comparing the loop operating status data with the loop status data before control execution. The degassing control parameters for the next control cycle are updated based on the control deviation. The degassing control parameters are updated according to the following adaptive update relationship:

[0052] in, These are the degassing control parameters for the current control cycle. For the target gas state variables, This is the preset update coefficient.

[0053] In this embodiment, by comparing the loop states before and after control execution, the system can evaluate the impact of the current control strategy on the gas state. In one specific embodiment, when the gas state quantity is still higher than the target state quantity after control execution, the degassing control parameters are adjusted accordingly in the next control cycle; when the gas state quantity approaches or reaches the target state quantity after control execution, the system maintains or reduces the subsequent control intensity, thereby enabling the degassing control process to have continuous and adaptive characteristics.

[0054] In some embodiments, the control cycle is a fixed time period preset based on the operating characteristics of the power battery thermal management system, and the loop status data, operating condition data, and gas state quantities are all updated synchronously within each control cycle. In a specific embodiment, the control cycle is set according to the response speed of the coolant loop and the vehicle operating status, so that the data update frequency can reflect changes in the loop operating status in a timely manner without placing an excessive burden on the system's computing resources. By completing data acquisition, status calculation, and control decision-making within a fixed control cycle, the degassing control process has good time consistency.

[0055] In some embodiments, when a certain type of loop status data or operating condition data is detected to be continuously missing, fluctuating abnormally, or experiencing communication interruptions during data acquisition or preprocessing, the system marks the corresponding data as valid and reduces the weight of this type of data in the fusion process or temporarily replaces it with historical valid data when generating a loop status snapshot. In one specific embodiment, when pressure status data is unavailable for multiple consecutive control cycles, the system still maintains the degassing control decision based on the gas state quantity and its trend prediction results, thereby avoiding degassing control interruption due to a single data source anomaly.

[0056] In some embodiments, the degassing control method adapts the control strategy according to the vehicle's operating phase. In one specific embodiment, when the vehicle is in the start-up or low-load operation phase, the system adopts relatively conservative degassing control parameters to avoid excessive disturbance to the coolant circuit; when the vehicle is in the high-load or continuous operation phase, the system allows for increased degassing control sensitivity while meeting safety constraints, thereby promptly suppressing gas accumulation trends. By differentiating between different operating phases, the degassing control process is matched to the actual operating state of the vehicle.

[0057] In some embodiments, after the abnormal safety handling control is completed, the system continuously monitors the operating status of the coolant circuit. When the circuit pressure, gas state quantity, and flow state are detected to have returned to the preset safe range, the system gradually exits the abnormal safety handling control level and returns to the normal degassing control level or normal monitoring state. In one specific embodiment, the system smoothly adjusts the degassing control parameters during the recovery process to avoid secondary fluctuations caused by directly switching from abnormal handling to normal operation.

[0058] In some embodiments, the system continuously stores loop state data, gas state variables, risk variables, prediction results, and degassing control execution records during operation to form a long-term operational data set. In one specific embodiment, the system periodically updates the gas state change model and threshold rules based on the long-term operational data, enabling the model parameters to gradually adapt to the operating characteristics of different vehicles, different usage environments, and different aging stages. By performing backtracking analysis on the operational data, the degassing control strategy is made capable of evolving over time.

[0059] Please see Figure 7 and Figure 8 As shown, in some embodiments, this application provides a power battery thermal management coolant circuit degassing control system for implementing a power battery thermal management coolant circuit degassing control method. The system includes: The intelligent sensing layer is configured to collect multi-source sensing data to characterize the gas state, pressure state and flow state of the coolant circuit within the same control cycle, and perform time alignment, filtering and outlier removal on the multi-source sensing data to generate circuit state data. The edge intelligent decision layer communicates with the intelligent perception layer and is configured to receive loop status data and power battery operating condition data, fuse them to generate gas state variables, build and update the gas state change model based on historical operating data, and obtain the predicted gas state variables. Risk quantities are generated based on gas state quantities, predicted gas state quantities, and loop pressure and flow change characteristics. Degassing control levels are determined and degassing control commands are generated according to preset threshold rules. The high-efficiency execution layer communicates with the edge intelligent decision-making layer and is configured to receive degassing control commands. When the degassing control level corresponds to normal degassing conditions, it performs vacuum staged degassing control and performs controllable pulse depressurization and directional exhaust control when the degassing control level corresponds to abnormal safety handling conditions. The safety protection and redundancy design layer is configured to monitor key operating parameters of the coolant circuit during system operation and trigger control strategy verification or degradation processing when an abnormal operating state is detected. The cloud platform and data closed-loop layer are configured to store and analyze system operation data and degassing event data, update the parameters and threshold rules of the gas state change model based on the analysis results, and send the updated results to the edge intelligent decision layer.

[0060] In some embodiments, the intelligent sensing layer further includes a hierarchical sensing unit for sensing and acquiring the operating status of different levels within the coolant circuit. The hierarchical sensing unit includes a gas sensing unit for detecting the composition and changes of gases in the coolant circuit, a pressure sensing unit for detecting pressure changes in the coolant circuit, and a flow sensing unit for detecting changes in coolant flow rate or velocity in the coolant circuit. In one specific embodiment, the gas sensing unit reflects the changing trends of dissolved and precipitated gases in the coolant, the pressure sensing unit reflects local or overall pressure fluctuations within the circuit, and the flow sensing unit reflects the impact of gas presence on the continuity of coolant flow, thereby providing a multi-dimensional sensing basis for subsequent decision-making.

[0061] In some embodiments, the edge intelligent decision layer runs on an in-vehicle hardware platform and performs gas state quantity calculations, gas state change model updates, and degassing control level determination locally. In one specific embodiment, the edge intelligent decision layer performs an adaptive learning process based on historical and real-time operating data, locally adjusting model parameters and threshold rules to enable the decision logic to adapt to changes in thermal management requirements under different vehicles, different usage environments, and different operating stages.

[0062] In some embodiments, the high-efficiency execution layer includes an execution unit for supporting multiple degassing control modes, and switches between different control modes according to control commands issued by the edge intelligent decision layer. In one specific embodiment, when a normal degassing control command is received, the high-efficiency execution layer enters the normal degassing mode, gradually reducing the gas content in the coolant circuit through vacuum staged degassing; when an abnormal safety handling control command is received, the high-efficiency execution layer enters the emergency degassing mode, depressurizing through a controllable pulse method and guiding the abnormal gas to be discharged in conjunction with a directional exhaust path. The execution process may further include post-processing operations on the discharged gas to meet system safety and environmental requirements.

[0063] In some embodiments, the safety protection and redundancy design layer includes multiple safety protection mechanisms to ensure the basic operational safety of the coolant circuit in the event of system anomalies or failures. In one specific embodiment, the safety protection and redundancy design layer continuously monitors key operating parameters, and triggers corresponding safety protection strategies when parameters are detected to exceed safe ranges. Simultaneously, by setting redundant pressure protection measures and emergency power supply support, the system can maintain basic degassing and monitoring capabilities even when some functions are limited. Furthermore, the safety protection and redundancy design layer can also degrade the degassing control strategy when a fault condition is detected to prevent the spread of abnormalities.

[0064] In some embodiments, the cloud platform and data closed-loop layer are used for centralized management of degassing operation data from multiple vehicles. In one specific embodiment, the cloud platform aggregates and analyzes loop state data, gas state quantities, risk quantities, and degassing event data from different vehicles to identify common operating patterns or potential risk trends, and continuously optimizes the gas state change model and threshold rules based on the analysis results. The optimized model parameters and rules can be distributed to the edge intelligent decision-making layer of the corresponding vehicle through a secure communication mechanism, thereby enabling cross-vehicle and cross-use scenario degassing control strategy updates.

[0065] In some embodiments, this application provides a terminal, including: The memory is used to store the degassing control program for the thermal management coolant circuit of the power battery; A processor is used to execute the steps of the power battery thermal management coolant circuit degassing control method when performing the power battery thermal management coolant circuit degassing control system.

[0066] In some embodiments, this application provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the power battery thermal management coolant circuit degassing control method.

[0067] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the protection scope of one or more embodiments of this specification.

Claims

1. A method for controlling the degassing of the coolant circuit in the thermal management system of a power battery, characterized in that, Includes the following steps: Acquire historical operating data, and acquire in real time circuit status data to characterize the gas generation and exhaust status of the power battery thermal management coolant circuit, as well as operating condition data to characterize the power battery operating conditions. The loop state data and operating condition data are preprocessed and fused to generate a gas state quantity to characterize the degree of gas accumulation in the coolant loop. Based on the gas state quantity, a risk quantity to characterize the risk of gas resistance in the loop is generated. Based on historical operating data, current loop status data, and operating condition data, the trend of gas state quantities is predicted to obtain gas trend prediction results, and the pre-start degassing criteria are determined accordingly. The gas state quantity, risk quantity, gas trend prediction results are combined with preset threshold rules to determine the degassing control level. When the degassing control level meets the normal degassing conditions, the degassing control parameters are determined based on the deviation between the gas state quantity and the target state quantity, and vacuum staged degassing control is executed. When the degassing control level meets the abnormal safety handling conditions, the handling control parameters are determined based on the circuit pressure and pressure change characteristics, and controllable pulse depressurization and directional exhaust control are executed.

2. The method for degassing control of the coolant circuit in the thermal management of a power battery according to claim 1, characterized in that, The steps for acquiring and preprocessing loop status data and operating condition data include: Within the same control cycle, multi-source raw data reflecting the gas state, pressure state, and flow state in the coolant circuit are acquired simultaneously. The original data from multiple sources are processed sequentially with timestamp alignment, filtering, and outlier identification. The identified outliers are then removed or replaced. The processed multi-source data is combined to generate a data set that characterizes the overall operating status of the coolant circuit under the current control cycle, and the data set is stored as a loop status snapshot.

3. The method for degassing control of the coolant circuit in the thermal management of a power battery according to claim 2, characterized in that, The steps for generating gaseous state variables include: Normalize the data reflecting changes in gas content in the coolant in the loop state snapshot and calculate the gas content change data between adjacent control cycles; Perform trend extraction processing on the data reflecting the bubble distribution and change characteristics in the loop state snapshot, and calculate the bubble distribution change data between adjacent control cycles; Based on the fusion weight, the gas content change data and bubble distribution change data are linearly combined to obtain the intermediate gas fusion amount; The pressure change characteristics of the coolant circuit between adjacent control cycles are introduced as a modulation factor to correct the intermediate gas fusion amount and generate gas state variables. Among them, gas state quantities Determined according to the following relationship: in, Data on changes in gas content, Data on bubble distribution changes, To represent the change in loop pressure, , and To integrate weights, This is the preset modulation coefficient. The gas state variables are updated in each control cycle.

4. The method for degassing control of the coolant circuit in the thermal management of a power battery according to claim 3, characterized in that, The process of generating risk parameters based on gas state parameters and predicting their trends includes the following steps: A gas state change model is constructed based on the time sequence of gas state variables in historical operating data. Within each control cycle, the gas state quantity acquired in the current control cycle is used as the initial state of the model, and the current operating condition data is used as the model input to update the gas state change model. Based on the updated gas state change model, the gas state variables are predicted for at least one subsequent control period. Based on the gas state variables in the current control cycle, the predicted gas state variables, and the pressure and flow change characteristics of the coolant circuit, calculate the risk quantity used to characterize the operating risk level of the coolant circuit. The gas state change model adopts a discrete state recursive form, which satisfies the following relationship: in, The data represents the current operating conditions, while A and B are state transition coefficients determined based on historical operating data. Risk level Determined according to the following relationship: in, , , These are preset weighting coefficients.

5. The method for degassing control of the coolant circuit in the thermal management of a power battery according to claim 4, characterized in that, The determination of pre-start degassing criteria includes: Based on the gas state change model, the predicted gas state quantities are obtained for multiple predictive control cycles within a preset prediction time window. Compare the predicted gas state quantity corresponding to each predictive control cycle with the preset gas prediction threshold. The number of control cycles in which the predicted gas state quantity exceeds the gas prediction threshold within the prediction time window is counted. when At that time, the pre-start degassing criterion is determined to be valid; in, This is an indicator function that takes the value 1 when the condition is true and 0 otherwise. For the predicted gas state quantity in the k-th predictive control cycle; N is the gas prediction threshold; M is the prediction time window length; and M is the preset number of consecutive judgments.

6. The method for degassing control of the coolant circuit in the thermal management of a power battery according to claim 5, characterized in that, Determine the degassing control level, including: Generate corresponding gas risk indicators based on gas state variables. Based on the characteristics of loop pressure change and flow, corresponding loop operation risk indicators are generated. Generate corresponding forecast risk indicators based on gas trend forecast results. The gas risk index, the loop operation risk index and the predicted risk index are weighted and summed to obtain a comprehensive risk score; The comprehensive risk score is compared with multiple preset risk ranges to determine the degassing control level. The comprehensive risk score is determined according to the following relationship: in, To predict the statistical values ​​of gas state quantities, , , These are preset weighting coefficients.

7. The method for degassing control of the coolant circuit in the thermal management of a power battery according to claim 6, characterized in that, The process of performing vacuum staged degassing control or controllable pulse depressurization and directional exhaust control also includes: After the control execution is completed, obtain the loop operation status data corresponding to the current control cycle; The control deviation is obtained by comparing the loop operating status data with the loop status data before control execution. The degassing control parameters for the next control cycle are updated based on the control deviation. The degassing control parameters are updated according to the following adaptive update relationship: in, These are the degassing control parameters for the current control cycle. For the target gas state variables, This is the preset update coefficient.

8. A degassing control system for the thermal management coolant circuit of a power battery, used to implement the degassing control method for the thermal management coolant circuit of a power battery as described in claim 1, characterized in that, The system includes: The intelligent sensing layer is configured to collect multi-source sensing data to characterize the gas state, pressure state and flow state of the coolant circuit within the same control cycle, and perform time alignment, filtering and outlier removal on the multi-source sensing data to generate circuit state data. The edge intelligent decision layer communicates with the intelligent perception layer and is configured to receive loop status data and power battery operating condition data, fuse them to generate gas state variables, build and update the gas state change model based on historical operating data, and obtain the predicted gas state variables. Risk quantities are generated based on gas state quantities, predicted gas state quantities, and loop pressure and flow change characteristics. Degassing control levels are determined and degassing control commands are generated according to preset threshold rules. The high-efficiency execution layer communicates with the edge intelligent decision-making layer and is configured to receive degassing control commands. When the degassing control level corresponds to normal degassing conditions, it performs vacuum staged degassing control and performs controllable pulse depressurization and directional exhaust control when the degassing control level corresponds to abnormal safety handling conditions. The safety protection and redundancy design layer is configured to monitor key operating parameters of the coolant circuit during system operation, and trigger control strategy verification or degradation processing when an abnormal operating state is detected. The cloud platform and data closed-loop layer are configured to store and analyze system operation data and degassing event data, update the parameters and threshold rules of the gas state change model based on the analysis results, and send the updated results to the edge intelligent decision layer.

9. A terminal, characterized in that, include: The memory is used to store the degassing control program for the thermal management coolant circuit of the power battery; A processor is used to implement the steps of the power battery thermal management coolant circuit degassing control method as described in claim 1 when executing the power battery thermal management coolant circuit degassing control device.

10. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the degassing control method for the power battery thermal management coolant circuit as described in claim 1.

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